论文标题
可解释的多价金属电池电极设计电压学习
Interpretable learning of voltage for electrode design of multivalent metal-ion batteries
论文作者
论文摘要
深度学习（DL）确实已经成为一种有力的工具，可以快速准确地从大数据（例如当前商业锂离子电池设计）中预测材料特性。但是，由于稀缺的MIB数据可用性和DL模型可解释性差，因此其对多价金属电池（MIBS）的实用性是最有希望的未来大规模储能解决方案。在这里，我们开发了一种可解释的DL模型，作为在小数据集限制（150〜500）下多价MIB（二价镁，钙，锌和三价铝）学习电极电压的有效方法。将实验结果作为验证，我们的模型比通常在小型数据集式中比DL更好的机器学习模型要准确得多。除了高精度外，我们的无功能工程DL模型也可以解释，该模型可以自动提取Atom共价半径作为电压学习的最重要特征，通过从神经网络层中可视化向量。提出的模型可能会加速使用较少数据和较少域知识限制的多价MIB材料的设计和优化，并将其用于http://batteries.2dmatpedia.org/的公开可用的在线工具套件。
Deep learning (DL) has indeed emerged as a powerful tool for rapidly and accurately predicting materials properties from big data, such as the design of current commercial Li-ion batteries. However, its practical utility for multivalent metal-ion batteries (MIBs), the most promising future solution of large-scale energy storage, is limited due to the scarce MIB data availability and poor DL model interpretability. Here, we develop an interpretable DL model as an effective and accurate method for learning electrode voltages of multivalent MIBs (divalent magnesium, calcium, zinc, and trivalent aluminum) at small dataset limits (150~500). Using the experimental results as validation, our model is much more accurate than machine-learning models which usually are better than DL in the small dataset regime. Besides the high accuracy, our feature-engineering-free DL model is explainable, which automatically extracts the atom covalent radius as the most important feature for the voltage learning by visualizing vectors from the layers of the neural network. The presented model potentially accelerates the design and optimization of multivalent MIB materials with fewer data and less domain-knowledge restriction, and is implemented into a publicly available online tool kit in http://batteries.2dmatpedia.org/ for the battery community.
